Domestic abuse in Scotland
Alifya Mukadam
Aim
To create data science workflow to study the trend of domestic abuse incidents and crimes in Scotland from 2003 to 2021, as well as the prevalence of domestic abuse across NHS health boards in 2021.
Load packages
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## layoutData acquisition
Domestic abuse data sets contain information on the number of domestic abuse incidents and crimes recorded by Scotland police, and the prevalence (Crude rate per 10,000 population) recorded from 2003 to 2021 across Scotland and NHS Scotland health boards. The data sets were downloaded from the Scottish Government (Scottish Crime Statistics) available on Scottish Public Health Observatory
library(here)## here() starts at /Users/alifyamukadam/Documents/GitHub/R_Project/R_reportDA_Scot_data <- read_csv(here("inputs/Domestic-abuse-data_Scotland.csv"))## Rows: 19 Columns: 11## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): area_code, area_type, area_name, period, type_definition, indicator
## dbl (5): year, numerator, measure, upper_confidence_interval, lower_confiden...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.DA_Scot_HB_data <- read_csv(here("inputs/Scoland-HB-data.csv"))## Rows: 266 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): area_code, area_type, area_name, period, type_definition, indicator
## dbl (5): year, numerator, measure, upper_confidence_interval, lower_confiden...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Prepare the data
glimpse(DA_Scot_data) %>%
summary()## Rows: 19
## Columns: 11
## $ area_code <chr> "S00000001", "S00000001", "S00000001", "S000…
## $ area_type <chr> "Scotland", "Scotland", "Scotland", "Scotlan…
## $ area_name <chr> "Scotland", "Scotland", "Scotland", "Scotlan…
## $ year <dbl> 2003, 2004, 2005, 2006, 2007, 2008, 2009, 20…
## $ period <chr> "2003/04 financial year", "2004/05 financial…
## $ type_definition <chr> "Crude rate per 10,000 population", "Crude r…
## $ indicator <chr> "Domestic abuse", "Domestic abuse", "Domesti…
## $ numerator <dbl> 41235, 43633, 45331, 48884, 49949, 53931, 51…
## $ measure <dbl> 81.4, 85.8, 88.7, 95.2, 96.6, 103.7, 99.2, 1…
## $ upper_confidence_interval <dbl> 82.1, 86.6, 89.5, 96.1, 97.5, 104.5, 100.1, …
## $ lower_confidence_interval <dbl> 80.6, 85.0, 87.9, 94.4, 95.8, 102.8, 98.4, 1…## area_code area_type area_name year
## Length:19 Length:19 Length:19 Min. :2003
## Class :character Class :character Class :character 1st Qu.:2008
## Mode :character Mode :character Mode :character Median :2012
## Mean :2012
## 3rd Qu.:2016
## Max. :2021
## period type_definition indicator numerator
## Length:19 Length:19 Length:19 Min. :41235
## Class :character Class :character Class :character 1st Qu.:50938
## Mode :character Mode :character Mode :character Median :58439
## Mean :55731
## 3rd Qu.:59981
## Max. :65251
## measure upper_confidence_interval lower_confidence_interval
## Min. : 81.4 Min. : 82.1 Min. : 80.6
## 1st Qu.: 97.9 1st Qu.: 98.8 1st Qu.: 97.1
## Median :108.8 Median :109.7 Median :107.9
## Mean :105.0 Mean :105.9 Mean :104.1
## 3rd Qu.:112.5 3rd Qu.:113.3 3rd Qu.:111.5
## Max. :119.4 Max. :120.3 Max. :118.5glimpse(DA_Scot_HB_data) %>%
summary()## Rows: 266
## Columns: 11
## $ area_code <chr> "S08000015", "S08000015", "S08000015", "S080…
## $ area_type <chr> "Health board", "Health board", "Health boar…
## $ area_name <chr> "NHS Ayrshire & Arran", "NHS Ayrshire & Arra…
## $ year <dbl> 2003, 2004, 2005, 2006, 2007, 2008, 2009, 20…
## $ period <chr> "2003/04 financial year", "2004/05 financial…
## $ type_definition <chr> "Crude rate per 10,000 population", "Crude r…
## $ indicator <chr> "Domestic abuse", "Domestic abuse", "Domesti…
## $ numerator <dbl> 2589, 3213, 3171, 3679, 3868, 3996, 4251, 44…
## $ measure <dbl> 70.4, 87.2, 85.9, 99.6, 104.3, 107.4, 114.1,…
## $ upper_confidence_interval <dbl> 73.2, 90.2, 89.0, 102.9, 107.7, 110.8, 117.6…
## $ lower_confidence_interval <dbl> 67.7, 84.2, 83.0, 96.4, 101.1, 104.1, 110.7,…## area_code area_type area_name year
## Length:266 Length:266 Length:266 Min. :2003
## Class :character Class :character Class :character 1st Qu.:2007
## Mode :character Mode :character Mode :character Median :2012
## Mean :2012
## 3rd Qu.:2017
## Max. :2021
## period type_definition indicator numerator
## Length:266 Length:266 Length:266 Min. : 21.0
## Class :character Class :character Class :character 1st Qu.: 858.8
## Mode :character Mode :character Mode :character Median : 3485.0
## Mean : 3980.8
## 3rd Qu.: 4982.5
## Max. :17412.0
## measure upper_confidence_interval lower_confidence_interval
## Min. : 10.10 Min. : 15.50 Min. : 6.30
## 1st Qu.: 60.38 1st Qu.: 64.97 1st Qu.: 54.70
## Median : 91.20 Median : 94.20 Median : 88.20
## Mean : 86.82 Mean : 91.28 Mean : 82.72
## 3rd Qu.:114.40 3rd Qu.:118.08 3rd Qu.:111.65
## Max. :153.40 Max. :155.70 Max. :151.10Data cleaning
Select and tidy the data to plot domestic abuse trend in Scotland
DA_trend_data <- DA_Scot_data %>%
select('area_name','year','measure') %>%
rename('Area'= 'area_name',
'Year' = 'year',
'Prevalence' = 'measure'
)
head(DA_trend_data)## # A tibble: 6 × 3
## Area Year Prevalence
## <chr> <dbl> <dbl>
## 1 Scotland 2003 81.4
## 2 Scotland 2004 85.8
## 3 Scotland 2005 88.7
## 4 Scotland 2006 95.2
## 5 Scotland 2007 96.6
## 6 Scotland 2008 104.Select and tidy the data to plot domestic abuse across Scotland NHS health boards in 2021
DA_2021_HB_data <- DA_Scot_HB_data %>%
filter(year == "2021") %>%
select('area_name','measure') %>%
mutate(area_name = gsub("NHS", "", area_name)) %>%
rename('NHS_Health_Board' = 'area_name',
'Prevalence' = 'measure') %>%
arrange(desc(Prevalence))
head(DA_2021_HB_data)## # A tibble: 6 × 2
## NHS_Health_Board Prevalence
## <chr> <dbl>
## 1 " Fife" 144.
## 2 " Forth Valley" 130.
## 3 " Lanarkshire" 129.
## 4 " Greater Glasgow & Clyde" 126.
## 5 " Tayside" 123.
## 6 " Ayrshire & Arran" 122.summary(DA_2021_HB_data)## NHS_Health_Board Prevalence
## Length:14 Min. : 40.40
## Class :character 1st Qu.: 89.95
## Mode :character Median :116.00
## Mean :103.73
## 3rd Qu.:124.97
## Max. :143.50Categorisation
DA_2021_HB_categorised_data <- DA_2021_HB_data %>%
mutate(Category = case_when(
Prevalence <= 89.95 ~ "Low",
Prevalence > 89.95 & Prevalence <= 124.97 ~ "Medium",
Prevalence > 124.97 ~ "High"))
head(DA_2021_HB_categorised_data)## # A tibble: 6 × 3
## NHS_Health_Board Prevalence Category
## <chr> <dbl> <chr>
## 1 " Fife" 144. High
## 2 " Forth Valley" 130. High
## 3 " Lanarkshire" 129. High
## 4 " Greater Glasgow & Clyde" 126. High
## 5 " Tayside" 123. Medium
## 6 " Ayrshire & Arran" 122. MediumData visualisation
p1 <- DA_trend_data %>%
ggplot(aes(x = Year,
y = Prevalence,
group = 1)) +
geom_line(colour = "purple") +
ggtitle("Figure 1: Trend of Domestic abuse in Scotland") +
xlab("Year")+
ylab("Number of domestic abuse incidents*") +
labs(caption = "*indicates crude rate per 10,000 population") +
scale_x_continuous (breaks = seq(2003, 2021, by=1) ) +
theme(panel.background = element_rect(fill = NA),
axis.line = element_line(linewidth = 0.3, colour = "grey80"))
p2 <- DA_2021_HB_categorised_data %>%
ggplot(aes(x = reorder(NHS_Health_Board, Prevalence),
y = Prevalence,
fill = Category,
group = 1)) +
geom_col(alpha = 0.6) +
coord_flip() +
ggtitle("Figure 2: Domestic abuse across NHS Scotland Health Boards in 2021") +
xlab("Health Boards")+
ylab("Number of domestic abuse incidents*") +
labs(caption = "*indicates crude rate per 10,000 population") +
scale_fill_viridis(discrete=TRUE,) +
theme(panel.background = element_rect(fill = NA),
axis.line = element_line(linewidth = 0.3, colour = "grey80"),
legend.position = "bottom")
p1
p2
Interactive plots for .html
ggplotly(p1, tooltip = c("x", "y"), width = 800, height = 600)Summary
According to UK Office for National Statistics, 26% of women and 15% of men experience some form of domestic abuse between the ages of 16 to 59. (1) Appropriate data visualisations are necessary to create awareness among law enforcement about the increasing trend of domestic abuse over the last 19 years in Scotland. Categorising health boards according to prevalence will enable law enforcement to allocate appropriate resources and develop strategies to address this healthcare issue.
Figure 1 is a line graph that accurately represents the rising trend in Scotland over the last 19 years. Figure 2 is a horizontal bar graph depicting prevalence (crude rate per 10,000 population) across Scotland’s NHS health boards in 2021 (most recent data available). The health boards are arranged in descending order of prevalence and categorised into High, Medium, and Low for quick interpretation of the data. The median prevalence in 2021 was 103.73 (143.50,40.40). Fife had the highest number of domestic abuse incidents and crime in 2021, while Orkney had the lowest prevalence rate in 2021. Clear and consistent use of a viridis color palette makes the visualizations easy to interpret, and accessible to all, including individuals with color blindness.
However, the data does not highlight variations in age, gender, socioeconomic factors, support services available, and type of abuse. Data is not available after the year 2021. It is also worth noting that this data could be underestimated as data collection relies on people reporting incidents of domestic abuse, which is often under reported. Data categorisation based on quantiles may not align with clinical or policy-relevant benchmarks. Extensive research is needed to inform new policies in the prevention of domestic violence. (2)
References
Elkin, M., 2018. Domestic abuse: Findings from the crime survey for England and Wales: Year ending March 2018. O”ice for National Statistics.
Gilchrist, E.L. and Kebbell, M., 2013. Domestic violence: current issues in definitions and interventions with perpetrators in the UK. Forensic Psychology, p.219.